Opposition theory and computational semiotics

Dan Assaf, Yochai Cohen, Marcel Danesi, Yair Neuman

Abstract


Opposition theory suggests that binary oppositions (e.g., high vs. low) underlie basic cognitive and linguistic processes. However, opposition theory has never been implemented in a computational cognitive-semiotics model. In this paper, we present a simple model of metaphor identification that relies on opposition theory. An algorithm instantiating the model has been tested on a data set of 100 phrases comprising adjective-noun pairs in which approximately a half represent metaphorical language-use (e.g., dark thoughts) and the rest literal language-use (e.g., dark hair). The algorithm achieved 89% accuracy in metaphor identification and illustrates the relevance of opposition theory for modelling metaphor processing.

Keywords


opposition theory; computational semiotics; metaphor identification

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DOI: http://dx.doi.org/10.12697/SSS.2015.43.2-3.01





SIGN SYSTEMS STUDIES. ISSN 1406-4243 (print), 1736-7409 (online). E-mail: sss@ut.ee. Postal address: Sign Systems Studies, Dept. of Semiotics, University of Tartu, Jakobi St. 2, 51014 Tartu, Estonia